JAACAP: Association of parent-rated sleep disturbances with ADHD symptoms, nine-year follow-up of a population-based cohort study
Gosling CJ, Cortese S, Konofal E, Lecendreux M & Faraone SV
03/05/2022
Supplement 2: Description of the Sample
Demographics
table1::table1(~ AGE_w1_label + AGE_w2_label + AGE_w3_label +
Sex_label +
PARENT_EDUC_label +
MARITAL_STATUS_PARENT_w1_label +
REPEAT_label +
NUMBER_SIBLINGS_w1_label | CCA_label,
render.missing = NULL,
data = df.table1, overall = "Overall")| Complete case (N=256) |
Missing data (N=799) |
Overall (N=1055) |
|
|---|---|---|---|
| Mean age at wave 1 | |||
| Mean (SD) | 9.18 (1.83) | 9.43 (1.78) | 9.37 (1.80) |
| Median [Min, Max] | 9.00 [6.00, 12.0] | 10.0 [6.00, 12.0] | 9.00 [6.00, 12.0] |
| Mean age at wave 2 | |||
| Mean (SD) | 13.2 (1.83) | 13.4 (1.78) | 13.4 (1.80) |
| Median [Min, Max] | 13.0 [10.0, 16.0] | 14.0 [10.0, 16.0] | 13.0 [10.0, 16.0] |
| Mean age at wave 3 | |||
| Mean (SD) | 18.1 (1.85) | 18.2 (1.77) | 18.2 (1.81) |
| Median [Min, Max] | 18.0 [15.0, 21.0] | 18.0 [15.0, 21.0] | 18.0 [15.0, 21.0] |
| Sex | |||
| Male | 134 (52.3%) | 411 (51.4%) | 545 (51.7%) |
| Female | 122 (47.7%) | 388 (48.6%) | 510 (48.3%) |
| Parental education | |||
| Elementary or secondary school | 47 (18.4%) | 194 (24.3%) | 241 (22.8%) |
| High school | 60 (23.4%) | 191 (23.9%) | 251 (23.8%) |
| College or university | 149 (58.2%) | 413 (51.7%) | 562 (53.3%) |
| Parental marital status at wave 1 | |||
| Marital life or married | 224 (87.5%) | 627 (78.5%) | 851 (80.7%) |
| Single | 7 (2.7%) | 46 (5.8%) | 53 (5.0%) |
| Divorced, separed or widowed | 25 (9.8%) | 83 (10.4%) | 108 (10.2%) |
| Has ever repeated a grade | |||
| No | 193 (75.4%) | 649 (81.2%) | 842 (79.8%) |
| Yes | 62 (24.2%) | 149 (18.6%) | 211 (20.0%) |
| Mean number of siblings at wave 1 | |||
| Mean (SD) | 2.25 (0.773) | 2.24 (0.843) | 2.24 (0.826) |
| Median [Min, Max] | 2.00 [1.00, 5.00] | 2.00 [1.00, 7.00] | 2.00 [1.00, 7.00] |
ADHD and sleep variables
table1::table1(~ ADHD_w1 + ADHD_w2 + ADHD_w3 +
ADHD_IN_w1 + ADHD_IN_w2 + ADHD_IN_w3 +
ADHD_HY_w1 + ADHD_HY_w2 + ADHD_HY_w3 +
SLEEP_w1 + SLEEP_w2 + SLEEP_w3 +
ADHD_DRUG_w1_label + ADHD_DRUG_w2_label + ADHD_DRUG_w3_label +
SLEEP_TRT_w1_label + SLEEP_TRT_w2_label + SLEEP_TRT_w3_label | CCA_label,
render.missing = NULL,
data = df.table1, overall = "Overall")| Complete case (N=256) |
Missing data (N=799) |
Overall (N=1055) |
|
|---|---|---|---|
| ADHD_w1 | |||
| Mean (SD) | 1.91 (5.14) | 1.92 (4.50) | 1.91 (4.67) |
| Median [Min, Max] | 0 [0, 30.0] | 0 [0, 32.0] | 0 [0, 32.0] |
| ADHD_w2 | |||
| Mean (SD) | 3.07 (5.62) | 2.82 (5.39) | 2.89 (5.45) |
| Median [Min, Max] | 0 [0, 32.0] | 0 [0, 36.0] | 0 [0, 36.0] |
| ADHD_w3 | |||
| Mean (SD) | 2.54 (4.66) | 2.07 (3.87) | 2.30 (4.27) |
| Median [Min, Max] | 0 [0, 31.8] | 0 [0, 24.0] | 0 [0, 31.8] |
| ADHD_IN_w1 | |||
| Mean (SD) | 1.05 (2.98) | 1.00 (2.60) | 1.02 (2.70) |
| Median [Min, Max] | 0 [0, 18.0] | 0 [0, 18.0] | 0 [0, 18.0] |
| ADHD_IN_w2 | |||
| Mean (SD) | 1.97 (3.76) | 1.74 (3.39) | 1.80 (3.49) |
| Median [Min, Max] | 0 [0, 18.0] | 0 [0, 18.0] | 0 [0, 18.0] |
| ADHD_IN_w3 | |||
| Mean (SD) | 1.47 (3.23) | 1.41 (3.09) | 1.43 (3.16) |
| Median [Min, Max] | 0 [0, 16.0] | 0 [0, 18.0] | 0 [0, 18.0] |
| ADHD_HY_w1 | |||
| Mean (SD) | 0.859 (2.59) | 0.912 (2.52) | 0.898 (2.54) |
| Median [Min, Max] | 0 [0, 18.0] | 0 [0, 16.0] | 0 [0, 18.0] |
| ADHD_HY_w2 | |||
| Mean (SD) | 1.09 (2.48) | 1.08 (2.61) | 1.09 (2.57) |
| Median [Min, Max] | 0 [0, 14.0] | 0 [0, 18.0] | 0 [0, 18.0] |
| ADHD_HY_w3 | |||
| Mean (SD) | 1.07 (2.27) | 0.668 (1.53) | 0.860 (1.93) |
| Median [Min, Max] | 0 [0, 18.0] | 0 [0, 8.00] | 0 [0, 18.0] |
| SLEEP_w1 | |||
| Mean (SD) | 0.821 (0.903) | 0.932 (0.928) | 0.879 (0.917) |
| Median [Min, Max] | 1.00 [0, 5.50] | 1.00 [0, 5.50] | 1.00 [0, 5.50] |
| SLEEP_w2 | |||
| Mean (SD) | 0.915 (1.03) | 0.831 (0.968) | 0.854 (0.985) |
| Median [Min, Max] | 1.00 [0, 5.00] | 0.500 [0, 5.50] | 0.571 [0, 5.50] |
| SLEEP_w3 | |||
| Mean (SD) | 1.20 (1.18) | 0.979 (1.11) | 1.08 (1.15) |
| Median [Min, Max] | 1.00 [0, 6.50] | 1.00 [0, 5.50] | 1.00 [0, 6.50] |
| ADHD_DRUG_w1_label | |||
| Medication | 0 (0%) | 3 (0.4%) | 3 (0.3%) |
| No medication | 256 (100%) | 572 (71.6%) | 828 (78.5%) |
| ADHD_DRUG_w2_label | |||
| Medication | 1 (0.4%) | 6 (0.8%) | 7 (0.7%) |
| No medication | 255 (99.6%) | 650 (81.4%) | 905 (85.8%) |
| ADHD_DRUG_w3_label | |||
| Medication | 0 (0%) | 0 (0%) | 0 (0%) |
| No medication | 256 (100%) | 279 (34.9%) | 535 (50.7%) |
| SLEEP_TRT_w1_label | |||
| Treatment | 18 (7.0%) | 15 (1.9%) | 33 (3.1%) |
| No treatment | 238 (93.0%) | 265 (33.2%) | 503 (47.7%) |
| SLEEP_TRT_w2_label | |||
| Treatment | 23 (9.0%) | 34 (4.3%) | 57 (5.4%) |
| No treatment | 228 (89.1%) | 622 (77.8%) | 850 (80.6%) |
| SLEEP_TRT_w3_label | |||
| Treatment | 17 (6.6%) | 16 (2.0%) | 33 (3.1%) |
| No treatment | 239 (93.4%) | 263 (32.9%) | 502 (47.6%) |
Pattern of missing data
Graphical representation
df.aux <- df[, c('ADHD_w1', 'ADHD_w2', 'ADHD_w3',
'SLEEP_w1', 'SLEEP_w2', 'SLEEP_w3',
'PARENT_EDUC',
'NUMBER_SIBLINGS_w1',
'AGE_w1',
'SEX',
'MARITAL_STATUS_PARENT_w1',
'REPEAT')]
names(df.aux)[names(df.aux) == "MARITAL_STATUS_PARENT_w1"] <- "MARITAL"
names(df.aux)[names(df.aux) == "NUMBER_SIBLINGS_w1"] <- "N_siblings"
names(df.aux)[names(df.aux) == "PARENT_EDUC"] <- "PAR_EDUC"
VIM::aggr(df.aux, col=c('#66D490','#D56956'),
sortVars = TRUE, numbers = FALSE,
sortComsb = TRUE,
labels = names(df.aux), cex.axis = .7, gap = 1,
ylab = c("Histogram of missing data","Pattern"))
##
## Variables sorted by number of missings:
## Variable Count
## ADHD_w3 0.4928909953
## SLEEP_w3 0.4928909953
## SLEEP_w1 0.4919431280
## ADHD_w2 0.1308056872
## SLEEP_w2 0.1298578199
## ADHD_w1 0.0407582938
## N_siblings 0.0407582938
## AGE_w1 0.0407582938
## MARITAL 0.0407582938
## REPEAT 0.0018957346
## PAR_EDUC 0.0009478673
## SEX 0.0000000000Assess whether each variable predicts missingness
pred <- c("AGE_w1", "AGE_w2", "AGE_w3",
"ADHD_w2", "ADHD_w3", "SLEEP_w1", "SLEEP_w2", "SLEEP_w3",
"SEX", "PARENT_EDUC", "REPEAT", "NUMBER_SIBLINGS_w1",
"MARITAL_STATUS_PARENT_w1")
GLM_miss <- function(x, dv) {
formula <- as.formula(paste("CCA", x, sep = " ~ "))
if (x != "MARITAL_STATUS_PARENT_w1") {
return(data.frame(
broom::tidy(glm(formula, data = df, family = binomial)))[2, c(1,2,5)])
} else {
return(data.frame(
broom::tidy(glm(formula, data = df, family = binomial)))[2:3, c(1,2,5)])
}
}
miss <- do.call(rbind, lapply(pred, GLM_miss, dv = "CCA"))
miss[miss$p.value<.05, ]## term estimate p.value
## 27 SLEEP_w3 0.1693296 0.02730126
## 29 PARENT_EDUC.L 0.2815765 0.03472292
## 212 MARITAL_STATUS_PARENT_w12 -0.8534308 0.03883817Supplement 3: Distribution of the Variables
SLEEP symptoms
ggplot(df_plot_SLEEP, aes(x = sleepsymptoms, fill = waves)) +
geom_histogram(alpha = 0.5, position = position_dodge2()) + theme_bw() +
ylab("N") + xlab("SLEEP symptoms") +
guides(fill=guide_legend("Wave")) +
theme(
axis.title.y = element_text(size = 11, hjust = 0.5, face="bold"),
axis.title.x = element_text(face = "bold", size = 11, hjust = 0.5),
legend.position="top",
legend.title = element_text(colour = "black", size= 10, face="bold")) +
coord_cartesian(xlim = c(0, 6)) +
ggtitle("Figure S1")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ADHD symptoms
ggplot(df_plot_ADHD, aes(x = adhdsymptoms, fill = waves)) +
geom_histogram(alpha = 0.5, position = position_dodge2()) + theme_bw() +
ylab("N") + xlab("ADHD symptoms") +
guides(fill=guide_legend("Wave")) +
theme(
axis.title.y = element_text(size = 11, hjust = 0.5, face="bold"),
axis.title.x = element_text(face="bold", size = 11, hjust = 0.5),
legend.position="top",
legend.title = element_text(colour="black", size=10, face="bold")) +
coord_cartesian(xlim = c(0, 36)) +
ggtitle("Figure S1")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Supplement 4: Preliminary Analyses
Heatmap
heatmaply::heatmaply_cor(
cor(df[, c('ADHD_w1', 'ADHD_w2', 'ADHD_w3',
'SLEEP_w1', 'SLEEP_w2', 'SLEEP_w3')],
method = "spearman",
use = "complete.obs"),
main = "Figure S2",
xlab = "",
ylab = "",
k_col = 6,
k_row = 6)## Registered S3 methods overwritten by 'registry':
## method from
## print.registry_field proxy
## print.registry_entry proxyCorrelation matrix
Correlation coefficients
round(RcmdrMisc::rcorr.adjust(as.matrix(df[, c('ADHD_w1', 'ADHD_w2', 'ADHD_w3',
'SLEEP_w1', 'SLEEP_w2', 'SLEEP_w3')]),
type = "spearman",
use = "complete.obs")$R[[1]], 3)## ADHD_w1 ADHD_w2 ADHD_w3 SLEEP_w1 SLEEP_w2 SLEEP_w3
## ADHD_w1 1.000 0.393 0.297 0.148 0.038 0.085
## ADHD_w2 0.393 1.000 0.381 0.098 0.221 0.117
## ADHD_w3 0.297 0.381 1.000 0.141 0.221 0.279
## SLEEP_w1 0.148 0.098 0.141 1.000 0.335 0.248
## SLEEP_w2 0.038 0.221 0.221 0.335 1.000 0.259
## SLEEP_w3 0.085 0.117 0.279 0.248 0.259 1.000Associated p-values
round(RcmdrMisc::rcorr.adjust(as.matrix(df[, c('ADHD_w1', 'ADHD_w2', 'ADHD_w3',
'SLEEP_w1', 'SLEEP_w2', 'SLEEP_w3')]),
type = "spearman",
use = "complete.obs")$R[[3]], 3)## ADHD_w1 ADHD_w2 ADHD_w3 SLEEP_w1 SLEEP_w2 SLEEP_w3
## ADHD_w1 NA 0.000 0.000 0.018 0.543 0.176
## ADHD_w2 0.000 NA 0.000 0.119 0.000 0.062
## ADHD_w3 0.000 0.000 NA 0.024 0.000 0.000
## SLEEP_w1 0.018 0.119 0.024 NA 0.000 0.000
## SLEEP_w2 0.543 0.000 0.000 0.000 NA 0.000
## SLEEP_w3 0.176 0.062 0.000 0.000 0.000 NASupplement 5: Primary Analysis
Model
RICLPM_PRIM <- '
SLEEPx =~ 1*SLEEP_w1 + 1*SLEEP_w2 + 1*SLEEP_w3
ADHDy =~ 1*ADHD_w1 + 1*ADHD_w2 + 1*ADHD_w3
wSLEEP_w1 =~ 1*SLEEP_w1
wSLEEP_w2 =~ 1*SLEEP_w2
wSLEEP_w3 =~ 1*SLEEP_w3
wADHD_w1 =~ 1*ADHD_w1
wADHD_w2 =~ 1*ADHD_w2
wADHD_w3 =~ 1*ADHD_w3
wSLEEP_w2 + wADHD_w2 ~ wSLEEP_w1 + wADHD_w1
wSLEEP_w3 + wADHD_w3 ~ wSLEEP_w2 + wADHD_w2
wSLEEP_w1 ~~ wADHD_w1
wSLEEP_w2 ~~ wADHD_w2
wSLEEP_w3 ~~ wADHD_w3
SLEEPx ~~ SLEEPx
ADHDy ~~ ADHDy
SLEEPx ~~ ADHDy
wSLEEP_w1 ~~ wSLEEP_w1
wADHD_w1 ~~ wADHD_w1
wSLEEP_w2 ~~ wSLEEP_w2
wADHD_w2 ~~ wADHD_w2
wSLEEP_w3 ~~ wSLEEP_w3
wADHD_w3 ~~ wADHD_w3
'Complete case analysis
RI-CLPM output
RICLPM_CCA <- lavaan::lavaan(RICLPM_PRIM,
data = df,
estimator = "WLSMV",
meanstructure = TRUE,
int.ov.free = TRUE)
lavaan::summary(RICLPM_CCA, standardized = TRUE, fit.measures = TRUE, ci = TRUE)## lavaan 0.6-9 ended normally after 172 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 26
##
## Used Total
## Number of observations 256 1055
##
## Model Test User Model:
## Standard Robust
## Test Statistic 0.349 0.820
## Degrees of freedom 1 1
## P-value (Chi-square) 0.555 0.365
## Scaling correction factor 0.425
## Shift parameter 0.000
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 122.611 82.867
## Degrees of freedom 15 15
## P-value 0.000 0.000
## Scaling correction factor 1.586
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 1.000
## Tucker-Lewis Index (TLI) 1.091 1.040
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000 0.000
## 90 Percent confidence interval - lower 0.000 0.000
## 90 Percent confidence interval - upper 0.138 0.160
## P-value RMSEA <= 0.05 0.665 0.502
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.007 0.007
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## SLEEPx =~
## SLEEP_w1 1.000 1.000 1.000
## SLEEP_w2 1.000 1.000 1.000
## SLEEP_w3 1.000 1.000 1.000
## ADHDy =~
## ADHD_w1 1.000 1.000 1.000
## ADHD_w2 1.000 1.000 1.000
## ADHD_w3 1.000 1.000 1.000
## wSLEEP_w1 =~
## SLEEP_w1 1.000 1.000 1.000
## wSLEEP_w2 =~
## SLEEP_w2 1.000 1.000 1.000
## wSLEEP_w3 =~
## SLEEP_w3 1.000 1.000 1.000
## wADHD_w1 =~
## ADHD_w1 1.000 1.000 1.000
## wADHD_w2 =~
## ADHD_w2 1.000 1.000 1.000
## wADHD_w3 =~
## ADHD_w3 1.000 1.000 1.000
## Std.lv Std.all
##
## 0.455 0.504
## 0.455 0.444
## 0.455 0.387
##
## 2.551 0.496
## 2.551 0.455
## 2.551 0.548
##
## 0.780 0.864
##
## 0.918 0.896
##
## 1.085 0.922
##
## 4.464 0.868
##
## 4.998 0.891
##
## 3.896 0.837
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wSLEEP_w2 ~
## wSLEEP_w1 0.279 0.150 1.851 0.064 -0.016 0.574
## wADHD_w1 -0.008 0.017 -0.491 0.624 -0.041 0.025
## wADHD_w2 ~
## wSLEEP_w1 0.278 0.585 0.475 0.635 -0.869 1.425
## wADHD_w1 0.508 0.106 4.782 0.000 0.300 0.716
## wSLEEP_w3 ~
## wSLEEP_w2 0.208 0.120 1.732 0.083 -0.027 0.443
## wADHD_w2 0.004 0.021 0.182 0.856 -0.037 0.044
## wADHD_w3 ~
## wSLEEP_w2 0.654 0.327 2.002 0.045 0.014 1.294
## wADHD_w2 0.216 0.110 1.965 0.049 0.001 0.431
## Std.lv Std.all
##
## 0.237 0.237
## -0.040 -0.040
##
## 0.043 0.043
## 0.454 0.454
##
## 0.176 0.176
## 0.017 0.017
##
## 0.154 0.154
## 0.277 0.277
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wSLEEP_w1 ~~
## wADHD_w1 0.857 0.565 1.518 0.129 -0.250 1.964
## .wSLEEP_w2 ~~
## .wADHD_w2 0.960 0.303 3.167 0.002 0.366 1.554
## .wSLEEP_w3 ~~
## .wADHD_w3 1.400 0.575 2.436 0.015 0.274 2.526
## SLEEPx ~~
## ADHDy 0.093 0.264 0.352 0.725 -0.425 0.611
## Std.lv Std.all
##
## 0.246 0.246
##
## 0.243 0.243
##
## 0.359 0.359
##
## 0.080 0.080
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .SLEEP_w1 0.821 0.056 14.546 0.000 0.710 0.931
## .SLEEP_w2 0.915 0.064 14.262 0.000 0.789 1.041
## .SLEEP_w3 1.200 0.074 16.315 0.000 1.056 1.344
## .ADHD_w1 1.907 0.321 5.934 0.000 1.277 2.537
## .ADHD_w2 3.069 0.351 8.736 0.000 2.380 3.758
## .ADHD_w3 2.539 0.291 8.724 0.000 1.969 3.110
## SLEEPx 0.000 0.000 0.000
## ADHDy 0.000 0.000 0.000
## wSLEEP_w1 0.000 0.000 0.000
## .wSLEEP_w2 0.000 0.000 0.000
## .wSLEEP_w3 0.000 0.000 0.000
## wADHD_w1 0.000 0.000 0.000
## .wADHD_w2 0.000 0.000 0.000
## .wADHD_w3 0.000 0.000 0.000
## Std.lv Std.all
## 0.821 0.909
## 0.915 0.893
## 1.200 1.020
## 1.907 0.371
## 3.069 0.547
## 2.539 0.545
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## SLEEPx 0.207 0.089 2.332 0.020 0.033 0.381
## ADHDy 6.509 3.575 1.821 0.069 -0.498 13.515
## wSLEEP_w1 0.608 0.152 3.997 0.000 0.310 0.906
## wADHD_w1 19.927 4.967 4.012 0.000 10.192 29.662
## .wSLEEP_w2 0.798 0.118 6.751 0.000 0.566 1.030
## .wADHD_w2 19.548 3.842 5.088 0.000 12.017 27.079
## .wSLEEP_w3 1.140 0.150 7.614 0.000 0.846 1.433
## .wADHD_w3 13.360 4.859 2.750 0.006 3.837 22.882
## .SLEEP_w1 0.000 0.000 0.000
## .SLEEP_w2 0.000 0.000 0.000
## .SLEEP_w3 0.000 0.000 0.000
## .ADHD_w1 0.000 0.000 0.000
## .ADHD_w2 0.000 0.000 0.000
## .ADHD_w3 0.000 0.000 0.000
## Std.lv Std.all
## 1.000 1.000
## 1.000 1.000
## 1.000 1.000
## 1.000 1.000
## 0.947 0.947
## 0.782 0.782
## 0.967 0.967
## 0.880 0.880
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000% of variance explained
lavInspect(RICLPM_CCA, "r2")## wSLEEP_w2 wADHD_w2 wSLEEP_w3 wADHD_w3 SLEEP_w1 SLEEP_w2 SLEEP_w3 ADHD_w1
## 0.053 0.218 0.033 0.120 1.000 1.000 1.000 1.000
## ADHD_w2 ADHD_w3
## 1.000 1.000Supplement 6: Additional Analyses
Models
Inattentive symptoms
RICLPM_IN<- '
SLEEPx =~ 1*SLEEP_w1 + 1*SLEEP_w2 + 1*SLEEP_w3
ADHDy =~ 1*ADHD_IN_w1 + 1*ADHD_IN_w2 + 1*ADHD_IN_w3
wSLEEP_w1 =~ 1*SLEEP_w1
wSLEEP_w2 =~ 1*SLEEP_w2
wSLEEP_w3 =~ 1*SLEEP_w3
wADHD_IN_w1 =~ 1*ADHD_IN_w1
wADHD_IN_w2 =~ 1*ADHD_IN_w2
wADHD_IN_w3 =~ 1*ADHD_IN_w3
wSLEEP_w2 + wADHD_IN_w2 ~ wSLEEP_w1 + wADHD_IN_w1
wSLEEP_w3 + wADHD_IN_w3 ~ wSLEEP_w2 + wADHD_IN_w2
wSLEEP_w1 ~~ wADHD_IN_w1
wSLEEP_w2 ~~ wADHD_IN_w2
wSLEEP_w3 ~~ wADHD_IN_w3
SLEEPx ~~ SLEEPx
ADHDy ~~ ADHDy
SLEEPx ~~ ADHDy
wSLEEP_w1 ~~ wSLEEP_w1
wADHD_IN_w1 ~~ wADHD_IN_w1
wSLEEP_w2 ~~ wSLEEP_w2
wADHD_IN_w2 ~~ wADHD_IN_w2
wSLEEP_w3 ~~ wSLEEP_w3
wADHD_IN_w3 ~~ wADHD_IN_w3
'Hyperactive-impulsive symptoms
RICLPM_HY<- '
SLEEPx =~ 1*SLEEP_w1 + 1*SLEEP_w2 + 1*SLEEP_w3
ADHDy =~ 1*ADHD_HY_w1 + 1*ADHD_HY_w2 + 1*ADHD_HY_w3
wSLEEP_w1 =~ 1*SLEEP_w1
wSLEEP_w2 =~ 1*SLEEP_w2
wSLEEP_w3 =~ 1*SLEEP_w3
wADHD_HY_w1 =~ 1*ADHD_HY_w1
wADHD_HY_w2 =~ 1*ADHD_HY_w2
wADHD_HY_w3 =~ 1*ADHD_HY_w3
wSLEEP_w2 + wADHD_HY_w2 ~ wSLEEP_w1 + wADHD_HY_w1
wSLEEP_w3 + wADHD_HY_w3 ~ wSLEEP_w2 + wADHD_HY_w2
wSLEEP_w1 ~~ wADHD_HY_w1
wSLEEP_w2 ~~ wADHD_HY_w2
wSLEEP_w3 ~~ wADHD_HY_w3
SLEEPx ~~ SLEEPx
ADHDy ~~ ADHDy
SLEEPx ~~ ADHDy
wSLEEP_w1 ~~ wSLEEP_w1
wADHD_HY_w1 ~~ wADHD_HY_w1
wSLEEP_w2 ~~ wSLEEP_w2
wADHD_HY_w2 ~~ wADHD_HY_w2
wSLEEP_w3 ~~ wSLEEP_w3
wADHD_HY_w3 ~~ wADHD_HY_w3
'Inattentive
RI-CLPM output
RICLPM_IN_FIT <- lavaan::lavaan(RICLPM_IN,
data = df,
estimator = "WLSMV",
meanstructure = TRUE,
int.ov.free = TRUE)
lavaan::summary(RICLPM_IN_FIT, standardized = TRUE, fit.measures = TRUE, ci = TRUE)## lavaan 0.6-9 ended normally after 147 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 26
##
## Used Total
## Number of observations 256 1055
##
## Model Test User Model:
## Standard Robust
## Test Statistic 0.240 0.573
## Degrees of freedom 1 1
## P-value (Chi-square) 0.624 0.449
## Scaling correction factor 0.420
## Shift parameter 0.000
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 121.256 80.464
## Degrees of freedom 15 15
## P-value 0.000 0.000
## Scaling correction factor 1.623
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 1.000
## Tucker-Lewis Index (TLI) 1.107 1.098
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000 0.000
## 90 Percent confidence interval - lower 0.000 0.000
## 90 Percent confidence interval - upper 0.131 0.150
## P-value RMSEA <= 0.05 0.720 0.577
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.007 0.007
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## SLEEPx =~
## SLEEP_w1 1.000 1.000 1.000
## SLEEP_w2 1.000 1.000 1.000
## SLEEP_w3 1.000 1.000 1.000
## ADHDy =~
## ADHD_IN_w1 1.000 1.000 1.000
## ADHD_IN_w2 1.000 1.000 1.000
## ADHD_IN_w3 1.000 1.000 1.000
## wSLEEP_w1 =~
## SLEEP_w1 1.000 1.000 1.000
## wSLEEP_w2 =~
## SLEEP_w2 1.000 1.000 1.000
## wSLEEP_w3 =~
## SLEEP_w3 1.000 1.000 1.000
## wADHD_IN_w1 =~
## ADHD_IN_w1 1.000 1.000 1.000
## wADHD_IN_w2 =~
## ADHD_IN_w2 1.000 1.000 1.000
## wADHD_IN_w3 =~
## ADHD_IN_w3 1.000 1.000 1.000
## Std.lv Std.all
##
## 0.449 0.497
## 0.449 0.438
## 0.449 0.381
##
## 1.567 0.527
## 1.567 0.418
## 1.567 0.485
##
## 0.783 0.868
##
## 0.921 0.899
##
## 1.088 0.924
##
## 2.529 0.850
##
## 3.405 0.908
##
## 2.829 0.875
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wSLEEP_w2 ~
## wSLEEP_w1 0.274 0.154 1.774 0.076 -0.029 0.576
## wADHD_IN_w1 0.003 0.034 0.082 0.934 -0.065 0.070
## wADHD_IN_w2 ~
## wSLEEP_w1 0.275 0.376 0.731 0.465 -0.463 1.013
## wADHD_IN_w1 0.607 0.148 4.103 0.000 0.317 0.897
## wSLEEP_w3 ~
## wSLEEP_w2 0.199 0.120 1.663 0.096 -0.036 0.434
## wADHD_IN_w2 0.014 0.028 0.508 0.612 -0.041 0.069
## wADHD_IN_w3 ~
## wSLEEP_w2 0.628 0.289 2.173 0.030 0.062 1.194
## wADHD_IN_w2 0.266 0.110 2.416 0.016 0.050 0.482
## Std.lv Std.all
##
## 0.233 0.233
## 0.008 0.008
##
## 0.063 0.063
## 0.451 0.451
##
## 0.169 0.169
## 0.044 0.044
##
## 0.205 0.205
## 0.320 0.320
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wSLEEP_w1 ~~
## wADHD_IN_w1 0.662 0.392 1.690 0.091 -0.106 1.431
## .wSLEEP_w2 ~~
## .wADHD_IN_w2 0.619 0.202 3.065 0.002 0.223 1.015
## .wSLEEP_w3 ~~
## .wADHD_IN_w3 1.143 0.388 2.944 0.003 0.382 1.903
## SLEEPx ~~
## ADHDy -0.068 0.210 -0.323 0.746 -0.479 0.343
## Std.lv Std.all
##
## 0.334 0.334
##
## 0.231 0.231
##
## 0.417 0.417
##
## -0.096 -0.096
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .SLEEP_w1 0.821 0.056 14.546 0.000 0.710 0.931
## .SLEEP_w2 0.915 0.064 14.262 0.000 0.789 1.041
## .SLEEP_w3 1.200 0.074 16.315 0.000 1.056 1.344
## .ADHD_IN_w1 1.048 0.186 5.634 0.000 0.683 1.412
## .ADHD_IN_w2 1.970 0.235 8.389 0.000 1.510 2.430
## .ADHD_IN_w3 1.466 0.202 7.253 0.000 1.070 1.862
## SLEEPx 0.000 0.000 0.000
## ADHDy 0.000 0.000 0.000
## wSLEEP_w1 0.000 0.000 0.000
## .wSLEEP_w2 0.000 0.000 0.000
## .wSLEEP_w3 0.000 0.000 0.000
## wADHD_IN_w1 0.000 0.000 0.000
## .wADHD_IN_w2 0.000 0.000 0.000
## .wADHD_IN_w3 0.000 0.000 0.000
## Std.lv Std.all
## 0.821 0.909
## 0.915 0.893
## 1.200 1.020
## 1.048 0.352
## 1.970 0.525
## 1.466 0.453
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## SLEEPx 0.202 0.091 2.226 0.026 0.024 0.379
## ADHDy 2.456 1.586 1.548 0.122 -0.653 5.565
## wSLEEP_w1 0.613 0.156 3.924 0.000 0.307 0.919
## wADHD_IN_w1 6.398 2.264 2.825 0.005 1.959 10.836
## .wSLEEP_w2 0.802 0.118 6.791 0.000 0.570 1.033
## .wADHD_IN_w2 8.973 1.658 5.413 0.000 5.724 12.223
## .wSLEEP_w3 1.143 0.150 7.602 0.000 0.848 1.437
## .wADHD_IN_w3 6.582 1.620 4.062 0.000 3.406 9.758
## .SLEEP_w1 0.000 0.000 0.000
## .SLEEP_w2 0.000 0.000 0.000
## .SLEEP_w3 0.000 0.000 0.000
## .ADHD_IN_w1 0.000 0.000 0.000
## .ADHD_IN_w2 0.000 0.000 0.000
## .ADHD_IN_w3 0.000 0.000 0.000
## Std.lv Std.all
## 1.000 1.000
## 1.000 1.000
## 1.000 1.000
## 1.000 1.000
## 0.945 0.945
## 0.774 0.774
## 0.966 0.966
## 0.823 0.823
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000% of variance explained
lavInspect(RICLPM_IN_FIT, "r2")## wSLEEP_w2 wADHD_IN_w2 wSLEEP_w3 wADHD_IN_w3 SLEEP_w1 SLEEP_w2
## 0.055 0.226 0.034 0.177 1.000 1.000
## SLEEP_w3 ADHD_IN_w1 ADHD_IN_w2 ADHD_IN_w3
## 1.000 1.000 1.000 1.000Hyperactive
RI-CLPM output
RICLPM_HY_FIT <- lavaan::lavaan(RICLPM_HY,
data = df,
estimator = "WLSMV",
meanstructure = TRUE,
int.ov.free = TRUE)
lavaan::summary(RICLPM_HY_FIT, standardized = TRUE, fit.measures = TRUE, ci = TRUE)## lavaan 0.6-9 ended normally after 110 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 26
##
## Used Total
## Number of observations 256 1055
##
## Model Test User Model:
## Standard Robust
## Test Statistic 0.229 0.385
## Degrees of freedom 1 1
## P-value (Chi-square) 0.632 0.535
## Scaling correction factor 0.595
## Shift parameter 0.000
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 85.311 65.681
## Degrees of freedom 15 15
## P-value 0.000 0.000
## Scaling correction factor 1.387
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 1.000
## Tucker-Lewis Index (TLI) 1.164 1.182
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000 0.000
## 90 Percent confidence interval - lower 0.000 0.000
## 90 Percent confidence interval - upper 0.130 0.141
## P-value RMSEA <= 0.05 0.726 0.649
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.006 0.006
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## SLEEPx =~
## SLEEP_w1 1.000 1.000 1.000
## SLEEP_w2 1.000 1.000 1.000
## SLEEP_w3 1.000 1.000 1.000
## ADHDy =~
## ADHD_HY_w1 1.000 1.000 1.000
## ADHD_HY_w2 1.000 1.000 1.000
## ADHD_HY_w3 1.000 1.000 1.000
## wSLEEP_w1 =~
## SLEEP_w1 1.000 1.000 1.000
## wSLEEP_w2 =~
## SLEEP_w2 1.000 1.000 1.000
## wSLEEP_w3 =~
## SLEEP_w3 1.000 1.000 1.000
## wADHD_HY_w1 =~
## ADHD_HY_w1 1.000 1.000 1.000
## wADHD_HY_w2 =~
## ADHD_HY_w2 1.000 1.000 1.000
## wADHD_HY_w3 =~
## ADHD_HY_w3 1.000 1.000 1.000
## Std.lv Std.all
##
## 0.465 0.515
## 0.465 0.453
## 0.465 0.395
##
## 1.229 0.475
## 1.229 0.496
## 1.229 0.541
##
## 0.774 0.857
##
## 0.914 0.891
##
## 1.081 0.919
##
## 2.279 0.880
##
## 2.152 0.868
##
## 1.911 0.841
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wSLEEP_w2 ~
## wSLEEP_w1 0.259 0.149 1.737 0.082 -0.033 0.552
## wADHD_HY_w1 -0.022 0.033 -0.688 0.492 -0.086 0.041
## wADHD_HY_w2 ~
## wSLEEP_w1 0.031 0.402 0.077 0.939 -0.758 0.820
## wADHD_HY_w1 0.287 0.114 2.523 0.012 0.064 0.510
## wSLEEP_w3 ~
## wSLEEP_w2 0.198 0.118 1.671 0.095 -0.034 0.430
## wADHD_HY_w2 0.005 0.053 0.104 0.917 -0.098 0.109
## wADHD_HY_w3 ~
## wSLEEP_w2 0.118 0.174 0.677 0.499 -0.224 0.459
## wADHD_HY_w2 0.049 0.141 0.346 0.730 -0.228 0.326
## Std.lv Std.all
##
## 0.220 0.220
## -0.056 -0.056
##
## 0.011 0.011
## 0.304 0.304
##
## 0.167 0.167
## 0.011 0.011
##
## 0.056 0.056
## 0.055 0.055
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wSLEEP_w1 ~~
## wADHD_HY_w1 0.264 0.236 1.119 0.263 -0.199 0.727
## .wSLEEP_w2 ~~
## .wADHD_HY_w2 0.326 0.176 1.851 0.064 -0.019 0.671
## .wSLEEP_w3 ~~
## .wADHD_HY_w3 0.291 0.247 1.179 0.238 -0.193 0.775
## SLEEPx ~~
## ADHDy 0.092 0.114 0.809 0.418 -0.131 0.315
## Std.lv Std.all
##
## 0.150 0.150
##
## 0.178 0.178
##
## 0.144 0.144
##
## 0.161 0.161
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .SLEEP_w1 0.821 0.056 14.546 0.000 0.710 0.931
## .SLEEP_w2 0.915 0.064 14.262 0.000 0.789 1.041
## .SLEEP_w3 1.200 0.074 16.315 0.000 1.056 1.344
## .ADHD_HY_w1 0.859 0.162 5.308 0.000 0.542 1.176
## .ADHD_HY_w2 1.092 0.155 7.052 0.000 0.788 1.395
## .ADHD_HY_w3 1.069 0.142 7.530 0.000 0.791 1.347
## SLEEPx 0.000 0.000 0.000
## ADHDy 0.000 0.000 0.000
## wSLEEP_w1 0.000 0.000 0.000
## .wSLEEP_w2 0.000 0.000 0.000
## .wSLEEP_w3 0.000 0.000 0.000
## wADHD_HY_w1 0.000 0.000 0.000
## .wADHD_HY_w2 0.000 0.000 0.000
## .wADHD_HY_w3 0.000 0.000 0.000
## Std.lv Std.all
## 0.821 0.909
## 0.915 0.892
## 1.200 1.020
## 0.859 0.332
## 1.092 0.441
## 1.069 0.471
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## SLEEPx 0.216 0.085 2.554 0.011 0.050 0.382
## ADHDy 1.510 1.036 1.457 0.145 -0.522 3.541
## wSLEEP_w1 0.599 0.148 4.037 0.000 0.308 0.889
## wADHD_HY_w1 5.194 1.195 4.345 0.000 2.851 7.537
## .wSLEEP_w2 0.796 0.118 6.752 0.000 0.565 1.027
## .wADHD_HY_w2 4.196 0.972 4.319 0.000 2.292 6.101
## .wSLEEP_w3 1.135 0.150 7.586 0.000 0.842 1.429
## .wADHD_HY_w3 3.624 1.472 2.462 0.014 0.739 6.509
## .SLEEP_w1 0.000 0.000 0.000
## .SLEEP_w2 0.000 0.000 0.000
## .SLEEP_w3 0.000 0.000 0.000
## .ADHD_HY_w1 0.000 0.000 0.000
## .ADHD_HY_w2 0.000 0.000 0.000
## .ADHD_HY_w3 0.000 0.000 0.000
## Std.lv Std.all
## 1.000 1.000
## 1.000 1.000
## 1.000 1.000
## 1.000 1.000
## 0.952 0.952
## 0.907 0.907
## 0.971 0.971
## 0.993 0.993
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000% of variance explained
lavInspect(RICLPM_HY_FIT, "r2")## wSLEEP_w2 wADHD_HY_w2 wSLEEP_w3 wADHD_HY_w3 SLEEP_w1 SLEEP_w2
## 0.048 0.093 0.029 0.007 1.000 1.000
## SLEEP_w3 ADHD_HY_w1 ADHD_HY_w2 ADHD_HY_w3
## 1.000 1.000 1.000 1.000Multiple imputation
Imputation model
df.mice.aux <- df[, c('ADHD_w1', 'ADHD_w2', 'ADHD_w3',
'SLEEP_w1', 'SLEEP_w2', 'SLEEP_w3',
'PARENT_EDUC',
'NUMBER_SIBLINGS_w1',
'AGE_w1',
'SEX',
'MARITAL_STATUS_PARENT_w1',
'REPEAT'
)]
df.imput <- mice::mice(df.mice.aux, m = 50)
mice.imp <- NULL
for(i in 1:df.imput$m) {
mice.imp[[i]] <- mice::complete(df.imput, action=i)
}
RICLPM_PRIM.fit.imput <- runMI(RICLPM_PRIM,
data = mice.imp,
fun = "lavaan",
estimator = "WLSMV",
meanstructure = TRUE,
int.ov.free = TRUE)RI-CLPM output
lavaan::summary(RICLPM_PRIM.fit.imput, ci = TRUE, rsquare = TRUE,
test = "D2", pool.robust = TRUE, standardized = TRUE)## lavaan.mi object based on 50 imputed data sets.
## See class?lavaan.mi help page for available methods.
##
## Convergence information:
## The model converged on 50 imputed data sets
##
## Heywood cases detected for data set(s) 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50
## These are not necessarily a cause for concern, unless a pooled estimate is also a Heywood case.
##
## Rubin's (1987) rules were used to pool point and SE estimates across 50 imputed data sets, and to calculate degrees of freedom for each parameter's t test and CI.##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err t-value df P(>|t|) ci.lower
## SLEEPx =~
## SLEEP_w1 1.000 1.000
## SLEEP_w2 1.000 1.000
## SLEEP_w3 1.000 1.000
## ADHDy =~
## ADHD_w1 1.000 1.000
## ADHD_w2 1.000 1.000
## ADHD_w3 1.000 1.000
## wSLEEP_w1 =~
## SLEEP_w1 1.000 1.000
## wSLEEP_w2 =~
## SLEEP_w2 1.000 1.000
## wSLEEP_w3 =~
## SLEEP_w3 1.000 1.000
## wADHD_w1 =~
## ADHD_w1 1.000 1.000
## wADHD_w2 =~
## ADHD_w2 1.000 1.000
## wADHD_w3 =~
## ADHD_w3 1.000 1.000
## ci.upper Std.lv Std.all
##
## 1.000 0.465 0.516
## 1.000 0.465 0.473
## 1.000 0.465 0.407
##
## 1.000 1.896 0.408
## 1.000 1.896 0.349
## 1.000 1.896 0.441
##
## 1.000 0.772 0.857
##
## 1.000 0.866 0.881
##
## 1.000 1.043 0.913
##
## 1.000 4.246 0.913
##
## 1.000 5.084 0.937
##
## 1.000 3.863 0.898
##
## Regressions:
## Estimate Std.Err t-value df P(>|t|) ci.lower
## wSLEEP_w2 ~
## wSLEEP_w1 0.141 0.102 1.373 145.399 0.172 -0.062
## wADHD_w1 0.028 0.014 2.004 284.627 0.046 0.001
## wADHD_w2 ~
## wSLEEP_w1 0.268 0.401 0.670 229.507 0.504 -0.521
## wADHD_w1 0.474 0.092 5.164 462.049 0.000 0.294
## wSLEEP_w3 ~
## wSLEEP_w2 0.230 0.079 2.912 121.397 0.004 0.074
## wADHD_w2 0.009 0.012 0.792 192.775 0.429 -0.014
## wADHD_w3 ~
## wSLEEP_w2 0.433 0.239 1.816 206.561 0.071 -0.037
## wADHD_w2 0.324 0.069 4.721 263.197 0.000 0.189
## ci.upper Std.lv Std.all
##
## 0.343 0.126 0.126
## 0.055 0.137 0.137
##
## 1.057 0.041 0.041
## 0.655 0.396 0.396
##
## 0.386 0.191 0.191
## 0.033 0.046 0.046
##
## 0.903 0.097 0.097
## 0.460 0.427 0.427
##
## Covariances:
## Estimate Std.Err t-value df P(>|t|) ci.lower
## wSLEEP_w1 ~~
## wADHD_w1 0.759 0.302 2.515 225.634 0.013 0.164
## .wSLEEP_w2 ~~
## .wADHD_w2 0.940 0.230 4.096 840.137 0.000 0.490
## .wSLEEP_w3 ~~
## .wADHD_w3 1.072 0.289 3.704 315.041 0.000 0.502
## SLEEPx ~~
## ADHDy 0.036 0.209 0.172 133.537 0.864 -0.377
## ci.upper Std.lv Std.all
##
## 1.354 0.232 0.232
##
## 1.391 0.239 0.239
##
## 1.641 0.307 0.307
##
## 0.448 0.041 0.041
##
## Intercepts:
## Estimate Std.Err t-value df P(>|t|) ci.lower
## .SLEEP_w1 0.848 0.036 23.449 244.811 0.000 0.777
## .SLEEP_w2 0.858 0.040 21.654 5205.523 0.000 0.781
## .SLEEP_w3 1.089 0.046 23.656 247.966 0.000 0.999
## .ADHD_w1 1.902 0.187 10.183 Inf 0.000 1.536
## .ADHD_w2 2.897 0.219 13.237 3673.754 0.000 2.468
## .ADHD_w3 2.343 0.173 13.503 292.915 0.000 2.001
## SLEEPx 0.000 0.000
## ADHDy 0.000 0.000
## wSLEEP_w1 0.000 0.000
## .wSLEEP_w2 0.000 0.000
## .wSLEEP_w3 0.000 0.000
## wADHD_w1 0.000 0.000
## .wADHD_w2 0.000 0.000
## .wADHD_w3 0.000 0.000
## ci.upper Std.lv Std.all
## 0.920 0.848 0.942
## 0.936 0.858 0.874
## 1.180 1.089 0.954
## 2.268 1.902 0.409
## 3.326 2.897 0.534
## 2.684 2.343 0.544
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err t-value df P(>|t|) ci.lower
## SLEEPx 0.216 0.066 3.252 109.290 0.002 0.084
## ADHDy 3.594 2.062 1.743 196.396 0.083 -0.473
## wSLEEP_w1 0.596 0.084 7.073 157.502 0.000 0.429
## wADHD_w1 18.030 3.139 5.743 574.686 0.000 11.864
## .wSLEEP_w2 0.717 0.074 9.717 205.634 0.000 0.572
## .wADHD_w2 21.555 2.912 7.402 3228.245 0.000 15.845
## .wSLEEP_w3 1.042 0.085 12.293 261.853 0.000 0.875
## .wADHD_w3 11.704 2.180 5.368 465.790 0.000 7.419
## .SLEEP_w1 0.000 0.000
## .SLEEP_w2 0.000 0.000
## .SLEEP_w3 0.000 0.000
## .ADHD_w1 0.000 0.000
## .ADHD_w2 0.000 0.000
## .ADHD_w3 0.000 0.000
## ci.upper Std.lv Std.all
## 0.348 1.000 1.000
## 7.661 1.000 1.000
## 0.762 1.000 1.000
## 24.197 1.000 1.000
## 0.863 0.958 0.958
## 27.265 0.834 0.834
## 1.208 0.957 0.957
## 15.988 0.784 0.784
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## R-Square:
## Estimate
## wSLEEP_w2 0.042
## wADHD_w2 0.166
## wSLEEP_w3 0.043
## wADHD_w3 0.216
## SLEEP_w1 1.000
## SLEEP_w2 1.000
## SLEEP_w3 1.000
## ADHD_w1 1.000
## ADHD_w2 1.000
## ADHD_w3 1.000